Lignocellulosic fibres surface interactions in enzymatic reaction using data-mining.

Lignocellulosic fibres modification focused so far essentially on the resulting material properties to create functional fibres instead of determining the reaction influencing parameters. Using a data-mining algorithm, surface chemical composition of the fibres after modifications was compared to multiple signals. A 24 h reaction at either 25 °C or 60 °C, pH5 was conducted in presence of trans-ferulic acid, laccase, and lignocellulosic fibres (flax, hemp, or cellulose) having different chemical surface composition. Dimers and trimers were detected in variable concentrations in the reaction filtrate and extractive. At 25 °C, crystalline cellulose, amorphous cellulose, xylans, mannans, and lignins were well correlated to specific reaction products while at 60 °C, only lignins and xylan were found correlated to reaction products. Fibres surface composition affected the extractive profile. Lignocellulosic surface composition influence on the product formed was unveiled using a data mining approach. This study presents a way to unveil non-evident chemical interface interaction in reactions.

Evaluation of Lignocellulosic Biomass Degradation by Combining Mid- and Near-Infrared Spectra by the Outer Product and Selecting Discriminant Wavenumbers Using a Genetic Algorithm.

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy provide useful information on the molecular composition of biological systems. Because they are sensitive to organic and mineral components, there is a growing interest in these techniques for the development of biomarkers that reflect intrinsic characteristics of plants and their mode of degradation. Due to their complexity and complementary nature, an important challenge is the combining of MIR and NIR information to identify discriminating wavenumbers in each wavenumber region, with the ultimate goal of assessing the biodegradation process of a lignocellulosic biomass at different time scales. This work investigates the potential of using the outer product to combine MIR and NIR spectra to highlight the connections between fundamental molecular vibrations and their combinations and bonds. Because this operation yields high-dimensional spectra, we propose to use a genetic algorithm to select the most discriminant wavenumbers within the degradation process. The results from two lignocellulosic biomasses with different biodegradation kinetics, miscanthus aerial parts and maize roots, confirm that the outer product combination of MIR and NIR spectral information allows a better discrimination of the biodegradation kinetic compared with the simple concatenation of MIR and NIR spectra or with the use of MIR or MIR spectral information separately. We show that the genetic algorithm selects wavenumbers that correspond to principal vibrations of chemical functional groups of compounds that undergo degradation/conversion during the biodegradation of the lignocellulosic biomass.